One question people often ask is where does someone gets inspiration / information / news. Maybe an equally important question is how to filter the overwhelming information and get most out of it, especially in a world where you can spend all day long just following the ins and outs chasing what’s happening realtime in the political area, sports space, and the business world.

When asked about the daily information diet, Mark Andreessen mentioned he himself is running an experiment of consuming information in a polarized way – completely stop reading newspapers, magazines, and basically anything with time horizon that’s, for example, between 5 mins an 5 years. So he reads social medias (a very short timespan info) and books (written 50 or 100 years ago).

The claim of “not reading newspapers and magazines” is funny though because they have a great potions overlapped – You will inevitably consume a lot of news when you are on social media from people you follow. So you are not actually missing out too much if not seeing the headlines of newspapers.

The real difference is the 2000 inbound startups Mark Andreessen reviewed each year from some of the by definition the smartest people from the domains they operate in. This is the most inclusive information that only he and his handful co-workers have access to. And it is hard to pick up a magazine to find similar interesting topics because things will only come out months or years after.

While the invention of internet breakthroughs the isolations and make information available and accessible to everyone, people’s ability of filtering those information isn’t equal. Those who come from upper classes or having different social capitals essentially still have a better filtration system that will keep them better off. The other side of the crowd might still be suffering by circling around low quality information and knowledge resulted from bad filtration capability.

What, then, are the washing machines of machine learning, for real companies? I think there are two sets of tools for thinking about this. The first is to think in terms of a procession of types of data and types of question:

Machine learning may well deliver better results for questions you’re already asking about data you already have, simply as an analytic or optimization technique. For example, our portfolio company Instacart built a system to optimize the routing of its personal shoppers through grocery stores that delivered a 50% improvement (this was built by just three engineers, using Google’s open-source tools Keras and Tensorflow).

Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for ‘angry’ emails, or ‘anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches,

Third, machine learning opens up new data types to analysis – computers could not really read audio, images or video before and now, increasingly, that will be possible.

Five years ago, if you gave a computer a pile of photos, it couldn’t do much more than sort them by size. A ten year old could sort them into men and women, a fifteen year old into cool and uncool and an intern could say ‘this one’s really interesting’. Today, with ML, the computer will match the ten year old and perhaps the fifteen year old. It might never get to the intern. But what would you do if you had a million fifteen year olds to look at your data? What calls would you listen to, what images would you look at, and what file transfers or credit card payments would you inspect?

Indeed, I think one could propose a whole list of unhelpful ways of talking about current developments in machine learning. For example:

Data is the new oil

Google and China (or Facebook, or Amazon, or BAT) have all the data

AI will take all the jobs

And, of course, saying AI itself.

More useful things to talk about, perhaps, might be:

Automation

Enabling technology layers

Relational databases.

Google ‘has all the data’, or that IBM has an actual thing called ‘Watson’. Really, this is always the mistake in looking at automation: with each wave of automation, we imagine we’re creating something anthropomorphic or something with general intelligence.

By the 1990s, pretty much all enterprise software was a relational database – PeopleSoft and CRM and SuccessFactors and dozens more all ran on relational databases. No-one looked at SuccessFactors or Salesforce and said “that will never work because Oracle has all the database” – rather, this technology became an enabling layer that was part of everything.

So, this is a good grounding way to think about ML today – it’s a step change in what we can do with computers, and that will be part of many different products for many different companies. Eventually, pretty much everything will have ML somewhere inside and no-one will care.

Year’s largest IPO – Xiaomi, starts trading publicly tomorrow. Though its valuation has been “downgraded” to $48B from what Lei Jun claimed to be $200B, I doubt how far this copycat can go, with its confusing business modal – sales of mobile phone holds 80% of company revenue yet counts only 2.7% profit.

I like to make jokes about Xiaomi’s micro-imitation of Apple inside out – from product design to human behaviors to promotion techniques. For example, Lei Jun’s choreography and speech given on stage, Xiaomi router cloning Apple’s Magic Trackpad, invitation and promotions design, and even how a hardware should be grasped or arranged in a photography..